Text Pairwise Preference

Given a prompt and two candidate responses, decide which is better. This is the data shape used for RLHF/DPO preference datasets.

Files

basic.py — pick the winner with no further structure.
with_rubric.py — pick based on an explicit rubric supplied in the instructions.
with_rationale.py — winner plus a one-sentence explanation.
dpo_jury.py — a jury of 5 model families emits trainer-ready DPO records: typed verdicts, both-orderings position debiasing, self-preference recusal, gold-pair calibration, and an agreement gate that routes contested pairs to human review.
jury_calibrated.py — calibration-first jury: three jurors are first scored on a balanced gold set (5 gold=a / 5 gold=b, both orderings) for gold accuracy, Brier score on verbalized confidence, and position bias; jurors below the accuracy floor are dropped, the survivors vote with accuracy-derived weights, and every record carries per-juror attribution.
jury_hardened.py — jury hardened for adversarial inputs and juror failure: candidate answers are fenced as data-not-instructions (one demo pair embeds a prompt injection so the run shows it losing on merits), a juror that cannot produce a valid verdict abstains instead of crashing the batch, and records proceed on a 2-of-3 quorum with per-record voted / abstained / failed attribution.

When to use

Building a preference dataset to fine-tune a reward model.
Comparing two model versions on a held-out prompt set.
Bake-offs between prompts.

If you want a single score against a rubric rather than a pairwise comparison, use _17_llm_as_judge/.

Run

bash

python cookbook/data_labeling/_05_text_pairwise_preference/basic.py
python cookbook/data_labeling/_05_text_pairwise_preference/with_rubric.py
python cookbook/data_labeling/_05_text_pairwise_preference/with_rationale.py
python cookbook/data_labeling/_05_text_pairwise_preference/dpo_jury.py
python cookbook/data_labeling/_05_text_pairwise_preference/jury_calibrated.py
python cookbook/data_labeling/_05_text_pairwise_preference/jury_hardened.py

Requires GOOGLE_API_KEY. dpo_jury.py additionally requires OPENAI_API_KEY, ANTHROPIC_API_KEY, GROQ_API_KEY, and MISTRAL_API_KEY. jury_calibrated.py and jury_hardened.py additionally require OPENAI_API_KEY and ANTHROPIC_API_KEY.